Awesome GitHub RepositoriesComputer Vision Annotation

CVAT 是一个开源计算机视觉标注工具和可视化数据集管理平台。它提供了一个自托管界面，用于标注图像、视频和 3D 数据，以创建视觉 AI 模型的数据集。 该平台具有 AI 辅助数据标注功能，可自动创建掩码和边界框，并利用插件系统连接外部机器学习模型。它包括一个基于共识的质量保证系统，通过比较独立标注来验证标签准确性。 该系统涵盖协作团队管理、通过任务分解进行项目组织以及远程云存储集成。它还提供用于程序化工作流控制以及以行业标准格式导入和导出数据的 REST API。

Labelme 是一个基于 Python 的图像标注工具，用于创建计算机视觉数据集。它作为语义分割的可视化编辑器，允许用户使用多边形、矩形、点和圆定义对象边界。该应用程序还可用作多光谱图像标注器，支持卫星和科学图像中使用的位深度较高的 TIFF 文件。 该工具集成了 AI 辅助标注功能，可自动创建掩码和多边形。这些功能允许通过文本提示或交互式点选择来生成形状，根据用户放置的正负点提出边界建议。 该软件涵盖了广泛的数据管理和标注任务，包括创建密集像素掩码、旋转边界框和视频帧序列。它包含一个将内部 JSON 状态持久化转换为 COCO 和 Pascal VOC 等标准数据集格式的管道。其他功能包括图像级分类标志、几何细化工具和批量图像导入。

This project is a collection of educational resources and implementation frameworks providing deep learning model recipes, code samples, and step-by-step guides for computer vision tasks. It organizes complex workflows into modular recipes and implementation guides to facilitate the building of image and video analysis models. The framework focuses on specialized vision capabilities, including an image similarity framework for fast retrieval and re-ranking, human pose estimation, and video action recognition. It also provides specific tools for crowd density estimation and document image clea

FastSAM is an image segmentation framework that uses convolutional neural networks to isolate visual elements and generate masks for detectable objects within images. It provides a system for both automatic all-object segmentation and promptable image segmentation. The project utilizes an inference-optimized architecture to reduce computational overhead, enabling faster mask generation and real-time visual analysis. It supports the creation of precise masks through various prompt inputs, including points, bounding boxes, and text descriptions. The framework covers broader computer vision cap

X-AnyLabeling is an AI-assisted annotation platform and computer vision labeling tool. It provides an interface for annotating images and videos using polygons and rectangles to create training sets for machine learning models. The project distinguishes itself through the integration of external AI models via a plugin-based inference backend, allowing for automated generation of candidate labels and the execution of specialized tasks like pose estimation and object detection. It also functions as an optical character recognition tool for extracting text and layout information from document im

This project is a toolkit and API designed for parsing, manipulating, and visualizing image annotations for computer vision tasks. It provides a programming interface to load and organize Common Objects in Context annotations, specifically for object detection, image segmentation, and keypoint estimation. The library includes tools for converting formatted JSON files into data structures that support the analysis of pixel-level masks and skeletal markers. It enables the visual verification of ground truth accuracy by rendering bounding boxes, segmentation masks, and keypoint markers directly

VoTT 是一款计算机视觉标注软件和机器学习数据集准备工具。它是一款桌面应用程序，专为在图像和视频中绘制边界框并为对象分配标签而设计，用于创建目标检测模型的训练数据集。 该应用利用跨平台桌面界面来管理图像和视频资源。它具有本地优先的存储集成，可直接从主机文件系统处理大型媒体资源，并包括帧率控制的视频采样，以从视频流中提取特定图像进行标注。 该软件涵盖了完整的数据生命周期，包括从本地或云存储导入资源，以及通过基于模式的导出将标注数据转换为各种机器学习格式。它还结合了基于令牌的加密来保护敏感的项目配置设置。

ISAT with Segment Anything is a desktop application designed for computer vision data labeling and interactive image segmentation. It provides a semi-automatic annotation suite that accelerates the creation of object masks, polygons, and metadata for training datasets. The application translates user actions into spatial constraints through interactive visual prompting with clicked points and bounding boxes, as well as text-based prompts. It operates via a model-agnostic inference pipeline that interchanges multiple vision and language backbone networks through a unified adapter layer. A desk